Hysteresis Modeling of Robotic Catheters based on Long Short-Term Memory Network for Improved Environment Reconstruction

Hysteresis Modeling of Robotic Catheters based on Long Short-Term Memory Network for Improved Environment Reconstruction

Wu, Di (Katholieke Universiteit Leuven)
Zhang, Yao (Technische Universität München)
Ourak, Mouloud (Katholieke Universiteit Leuven)
Niu, Kenan (Katholieke Universiteit Leuven)
Dankelman, J. (TU Delft Medical Instruments & Bio-Inspired Technology)
Vander Poorten, Emmanuel B. (Katholieke Universiteit Leuven)

2021

Catheters are increasingly being used to tackle problems in the cardiovascular system. However, positioning precision of the catheter tip is negatively affected by hysteresis. To ensure tissue damage due to imprecise positioning is avoided, hysteresis is to be understood and compensated for. This work investigates the feasibility to model hysteresis with a Long Short-Term Memory (LSTM) network. A bench-top setup containing a catheter distal segment was developed for model evaluation.The LSTM was first tested using four groups of test datasets containing diverse patterns. To compare with the LSTM, a Deadband Rate-Dependent Prandtl-Ishlinskii (DRDPI) model and a Support Vector Regression (SVR) model were established. The results demonstrated that the LSTM is capable of predicting the tip bending angle with sub-degree precision. The LSTM outperformed the DRDPI model and the SVR model by 60.1% and 36.0%, respectively, in arbitrarily varying signals. Next, the LSTM was further validated in a 3D reconstruction experiment using Forward-Looking Optical Coherence Tomography (FL-OCT). The results revealed that the LSTM was able to accurately reconstruct the environment with a reconstruction error below 0.25 mm. Overall, the proposed LSTM enabled precise free-space control of a robotic catheter in the presence of severe hysteresis. The LSTM predicted the catheter tip response precisely based on proximal input pressure, minimizing the need to install sensors at the catheter tip for localization.

Catheters
coronary artery disease
Hysteresis
hysteresis
Logic gates
LSTM
modeling
Muscles
pneumatic artificial muscle
robotic catheter
Robots
Sensors
Solid modeling

http://resolver.tudelft.nl/uuid:6eac7164-8a4c-4348-91ff-13e225ef8597

https://doi.org/10.1109/LRA.2021.3061069

2021-08-22

2377-3766

IEEE Robotics and Automation Letters, 6 (2), 2106-2113

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2021 Di Wu, Yao Zhang, Mouloud Ourak, Kenan Niu, J. Dankelman, Emmanuel B. Vander Poorten